arXiv:2509.14760v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in diverse real-world scenarios, each governed by bespoke behavioral and safety specifications (spec) custom-tailored by users or organizations. These spec, categorized into safety-spec and behavioral-spec, vary across scenarios and evolve with changing preferences and requirements. We formalize this challenge as specification alignment, focusing on LLMs' ability to follow dynamic, scenario-specific spec from both behavioral and safety perspectives. To address this challenge, we propose Al
The increasing deployment of LLMs in diverse, real-world applications highlights the urgent need for models to adhere to dynamic and context-specific specifications.
Ensuring LLMs can reliably align with evolving behavioral and safety specifications is critical for their safe and effective integration across various sectors and for preventing unintended consequences.
The proposed 'Test-time Deliberation' method could significantly improve LLM fidelity to user-defined and situation-specific rules, enhancing their trustworthiness and applicability in sensitive domains.
- · AI developers
- · Organizations deploying LLMs
- · Users of LLM-powered applications
- · LLM providers with poor specification alignment
- · Sectors reliant on static AI models
LLMs become more reliable and adaptable to specific user and organizational requirements.
Increased adoption of LLMs in highly regulated or safety-critical industries due to enhanced control and predictability.
The development of 'specification alignment' tools and services becomes a new, significant segment within the AI industry, fostering deeper integration of AI into complex operational frameworks.
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